Various sensors of autonomous vehicle are vulnerable to spoofing attacks. Prior work mainly focused on the study of security in perception sensors, primarily the LiDAR and camera. Physical world camera attacks were demonstrated by fooling Deep Neural Networks (DNNs), making objects mislabeled or ignored [1]}, [2]}. For example, in [2]}, the researchers caused a “STOP” sign disappear in the eyes of the detector, by adding adversarial stickers onto the sign. Attacks against LiDAR-based perception systems in AVs appear in [4]}, [5]}. The authors found that simply spoofing the LiDAR using common methods is not sufficient due to the use of machine learning object detection models. To mount an attack they leveraged an optimization method to generate adversarial examples to fool a real world working LiDAR.
